What metric can you adjust in Nearby Clusters to show clusters closer in conceptual similarity to the selected center cluster?

The best choice for adjusting how clusters relate in terms of conceptual similarity to a selected center cluster is the Similarity Score. This metric specifically measures the degree of similarity between clusters based on their content, themes, or features. By manipulating the Similarity Score, you can effectively steer the visualization or analysis to highlight clusters that are conceptually closer to the selected center cluster, thus enabling a better understanding of relationships and relevance in the data.

The other metrics mentioned do not directly pertain to the conceptual closeness of clusters. Cluster Size relates to the number of documents within each cluster, which can inform about the representation but does not indicate similarity. Depth Value typically refers to the hierarchical level within a clustering structure rather than similarity between clusters. Document Count conveys the quantity of documents but lacks the nuanced understanding of how closely aligned the clusters' contents are in terms of themes or principles.

Thus, utilizing the Similarity Score provides a targeted approach to explore clusters that share more meaningful conceptual commonalities with the reference cluster.